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Mathematical Foundations of Data Science Using R

scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data;

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Mathematical Foundations of Data Science Using R

worldwide importance. The book gives the mathematical foundations to handle data properly. It introduces basics and functionalities of the R;

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Beginning Data Science in R 4

number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern;

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Doing Data Science in R

This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry;

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Beginning Data Science in R 4

github.com/Apress/beg-data-science-r4. What You Will Learn Perform data science and analytics using statistics and the R programming language;

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Mathematical Foundations of Computer Science 2006: 31st International Symposium, Mfcs 2006, Stará Lesná, Slovakia, August 28-September 1, 2006, Procee

in theoretical computer science and its mathematical foundations are addressed, from algorithms and data structures, to complexity, automata, semantics;

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Data Science Using Python and R

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world's two most widespread;

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Applied Statistics for Environmental Science with R

> Shows how to analyze data using R software and provides R scripts for all examples and figures;

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R 4 Data Science Quick Reference

data using magrittr and dplyr Write functions with R for data science, data mining, and analytics-based applications Visualize data with;

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R 4 Data Science Quick Reference

then transform that data using magrittr and dplyr Write functions with R for data science, data mining, and analytics-based applications;

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Machine Learning Using R

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition;

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R Data Science Quick Reference

. After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R;

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Geometric and Topological Inference

Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly;

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Geometric and Topological Inference

Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly;

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R Data Science Essentials

Learn the essence of data science and visualization using R in no time at all About This Book * Become a pro at making stunning;

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Statistics for Linguists

mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this;

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Statistics for Linguists

mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this;

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Easy Statistics for Food Science with R

>Shows how to analyze data using R software Provides R scripts for all examples and figures ;

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Using R for Numerical Analysis in Science and Engineering

engineering data analysis using R.;

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Data Science in Theory and Practice - Techniques for Big Data Analytics and Complex Data Sets

and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various;

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An Introduction to Data Analysis in R

This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics;

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Data Science for Business With R

Data Science for Business with R focuses on the concepts foundational for students starting a business analytics or data science course;

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Biostatistics for Human Genetic Epidemiology

examples. This clear and concise book covers human genetic epidemiology, using R in data analysis, including multivariate;

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Mathematical Foundations of Computer Science 2011

This volume constitutes the refereed proceedings of the 36th International Symposium on Mathematical Foundations of Computer Science, MFCS;

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The Mathematics of Data

Data science is a highly interdisciplinary field, incorporating ideas from applied mathematics, statistics, probability, and computer;

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The Big RBook

introduction to R, machine learning, mathematical models, data wrangling, and reporting Uses a practical tone and integrates multiple topics in a;

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Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the;

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